吕琳媛教授主要从事网络信息挖掘和社会经济复杂性方面的研究，原创性地提出以系综理论和似然分析为基础的网络信息挖掘基础理论体系，以及以扩散动力学为基础的网络信息挖掘系列方法。在《国家科学评论》、《物理报道》、《美国科学院院刊》、《自然通讯》等国内外期刊发表论文70余篇，SCI他引5000余次，谷歌学术引用10000余次，成果入选中国百篇最具影响国际学术论文。申请发明专利12项。专著《链路预测》获中国大学出版社优秀图书一等奖，获国家优秀青年基金，入选《麻省理工科技评论》中国35岁以下科技创新35人，担任国际网络科学学会理事、英国物理学会期刊《物理学报：复杂性》高级顾问编辑、《国家科学评论》特邀编辑、《国际现代物理C》副主编等学术职务。

She has utilized the theories and methods of statistical physics to address a number of important issues in information sciences. She originally proposed the basic theoretical system of network information filtering based on ensemble theory and likelihood analysis, and a series of network information filtering techniques based on diffusion dynamics on networks. The applicant has published more than 70 research papers, of which the majority are published in prestigious peer-reviewed international journals, like National Science Review, Physics Reports, PNAS, Nature Communications, etc.. All her publications get more than 5000 SCI non-self-citations, and more than 10000 citations from Google Scholar. Her researches were selected into one hundred of the most influential international academic papers in China. She has applied for 12 invention patents and 5 of them were granted. Her academic monograph "link prediction" won the first prize of excellent academic book award of the 4th China university press. She won the national science Fund for excellent young scholars in 2016. She also awarded the "MIT Technology Review Innovator Under 35 China" (MIT TR 35). Now the applicant serves as a board member of the International Network Science Society. She is also the senior consultant editor of Journal Physics: Complexity, a journal of the British Physical Society, the guest editor of National Science Review, and the associate editor ofInternational Journal of Modern Physics C.

Abstract: Recently, the research on vital nodes identification on complex networks has attracted much attention due to its theoretical importance and practical significance. The vital nodes of the network refer to some special nodes that can influence the network structure and function to a greater extent than other nodes of the network. How to find these nodes? What roles do they play in the structure and function of the network? This talk will introduce some of our representative works in this field from the perspectives of node degree and node circle respectively and discuss some future directions.